Method of extracting warehouse in port from hierarchically screened remote sensing image

ABSTRACT

A method of extracting a warehouse in a port from a hierarchically screened remote sensing image includes the following steps: first, recognizing a texture feature of a remote sensing image and extracting edge lines of a coast of a port; then, selecting a sample of an optional irregular texture region and forming, through a CA transformation, principal component images of different hierarchies by taking a ratio of a between-class difference to an intra-class difference being maximum as an optimization condition; sequentially, extracting a correlation relationship of the warehouse in the port, and forming a feature point set with recognized warehouses to be analyzed; and last, extracting a feature of a visually sensitive image through a scene image to obtain a feedback selection of a real scene image to extract the warehouse in the port accurately.

CROSS REFERENCE TO THE RELATED APPLICATIONS

The present application claims priority to Chinese Patent ApplicationNo. 2017112564776 filed on Dec. 4, 2017, the entire content of which isincorporated herein by reference.

TECHNICAL FIELD

The present invention relates to the field of remote sensingtechnologies, and more particularly to a method of extracting awarehouse in a port from a hierarchically screened remote sensing image.

BACKGROUND

Remote sensing images have been widely used in various aspects, and willbe used further as remote sensing image recognition technologies developfurther. In applications of remote sensing, information is collectedwithout directly contacting a related target, and the collectedinformation can be interpreted, classified and recognized. With the useof a remote sensing technology, a great quantity of earth observationdata can be acquired rapidly, dynamically, and accurately.

As a hub for marine transportation, port plays an extremely importantrole and is therefore received more and more attentions, becoming animportant research direction in marine transportation traffic planning.In the establishment and planning of a port, port data should becollected first, that is, the various ground objects in a port and theirpositions should be acquired, a logistics warehouse behind a storageyard is an important ground object in a port, and moreover, logisticswarehouses are also crucial for a port.

However, it is somewhat difficult to recognize, based on a remotesensing image, a warehouse in the rear of a port, in the prior art, forexample, a method of extracting an image of a logistics warehouse behinda storage yard in a port, which is disclosed in Patent Application No.201610847354.9, includes: (1) applying a lee sigma edge extractionalgorithm to a waveband of a remote sensing image, the algorithm using aspecific edge filter to create two independent edge images: abright-edged image and a dim-edged image, from the original image; (2)carrying out a multi-scale segmentation for the bright-edged image andthe dim-edged image together with the remote sensing image to obtain animage object; (3) classifying the ones of the obtained image objectshaving a big blue waveband ratio into a class A, and removing, using abrightness mean feature, the ones in the class A having a relative lowbrightness mean from the class A; (4) and removing the objects smallerthan a specified threshold from the class A using the NormalizedDifference Vegetation Index (NDVI) to obtain the category of a warehousewith a blue roof. Based on features of data and those of a logisticswarehouse behind a storage yard in a port, an image can be extractedaccurately at a high processing efficiency.

Taking an overall view of the foregoing technical solution, actuallyexisting problems and the currently widely used technical solutions, thefollowing major defects are found:

(1) first, no special method is currently available for the remotesensing reorganization of a logistics warehouse in the rear of a port,because existing methods are applicable to recognize other groundobjects and incapable of accurately recognizing a warehouse in a portaccording to features of the warehouse in the port, moreover, because ofthe lack of pertinence, the processing of remotely sensed big data islow in efficiency;

(2) second, most of existing data processing methods are based on thedirect extraction of a remote sensing image, the biggest defect of thisextraction mode is heave original data processing workload, and someundesired data or data out of this scope are usually taken intoconsideration during this calculation process, thus further increasingthe complicity of data processing; and

(3) last, in an existing data processing process, most of featureprocessing operations are based on spectral features, althoughfull-color remote sensing images have been developed, spectral featureis still disadvantaged in insufficient spectral information, making itnecessary to conduct an advanced computation and an interpolationoperation for an approximation recovery during a recognition process,however, this process usually triggers a correction algorithm, thus, toobtain a recognized feature that is close to reality, a large amount ofcalculation needs to be executed, furthermore, an algorithm correctionis circulated during this process, leading to a larger computation load.

SUMMARY

A technical solution adopted by the present invention to solve thetechnical problems is a method of extracting a warehouse in a port froma hierarchically screened remote sensing image, comprising the followingsteps:

S100: extracting a coastline of the port based on an active contourmodel, successively performing texture feature recognition on any regionin a remote sensing image to form a sea area texture region and anirregular texture region, and extracting edge lines of the coastline ofthe port;

S200: extracting principal component images of a plurality ofhierarchies using insufficient spectrum features, optionally selecting asample of the irregular texture region, and forming, through a CAtransformation, principal component images of different hierarchies withdifferent difference values by taking the ratio of a between-classdifference to an intra-class difference being maximum as an optimizationcondition;

S300: accurately recognizing the warehouse in the port using a spatialrelationship feature, extracting a correlation relationship of thewarehouse in the port from the principle component images, and forming afeature point set with recognized warehouses to be analyzed;

S400: extracting a feature of a visually sensitive image from thefeature point set through a scene image based on WTA visual rapidadaptation selection to obtain a feedback selection of a real sceneimage to extract the warehouse in the port.

As a preferred technical solution of the present invention, in stepS100, a gray level co-occurrence matrix is used to recognize a texturefeature, and the recognition includes the following steps:

S101: optionally selecting a region of the remote sensing image, andsetting that the region has L gray level values, in this case, a graylevel co-occurrence matrix corresponding to the region is a matrixhaving LXL orders;

S102: selecting an optional position (i,j) in the matrix, where (i, j=1,2, . . . , L), in this case, an element at the optional position is apixel at a fixed distance from a pixel having a gray level of i and hasa gray level of j, wherein the following fixed positional relationshipexists between the two pixels: ζ=(DX, DY), where ζ is a displacement,and DX and XY are distances in two directions;

S103: extracting, according to a positional relationship between thegray level co-occurrence matrixes, a texture feature quantity such as anAngular Second Moment (ASM) and a contrast CON, wherein ASM=Σ_(i=1)^(L)Σ_(j=1) ^(L)P² (i, j) and CON=Σ_(n=1) ^(L)n²[Σ_(i=1) ^(L)Σ_(j=1)^(L)P(i, j)]|i−j|, where P is a feature vector at the position (i,j),and n is the number of times extraction is performed.

As a preferred technical solution of the present invention, in stepS100, extracting edge lines of the coast of the port between the seaarea texture region and the irregular texture region using the filteralgorithm and optimizing the edge lines of the coast of the port usingthe filter algorithm specifically includes the following steps: first,acquiring discrete data of a texture feature and selecting a lowestcenter frequency when extracting an image feature in a filter using adiscretized Gabor template matrix and an image data matrix convolution,and then carrying out a frequency spectrum superposition calculationagain to obtain a filtered image.

As a preferred technical solution of the present invention, in stepS200, before the CA transformation is executed, the maximized ratio of abetween-class variance of an optional data set to an intra-classvariance of the data set is extracted according to the following lineartransformation formula: Y=TX, where T is an ideal transformation matrix,so as to ensure the maximum separability of the data set to provideoptimized basic data for the CA transformation.

As a preferred technical solution of the present invention, a specificalgorithm of the ideal transformation matrix is as follows:

S201: σ_(A) is set as a standard deviation of a class 1 and a class 2obtained after the transformation, σ_(w1) and σ_(w2) are set asintra-class standard deviations of the class 1 and the class 2, andσ_(w) is set as the average value of σ_(w1) and σ_(w2);

S202: the relationship between a transformed variance and anuntransformed variance is as follows:

σ_(w) ²=t^(T)S_(w)t, σ_(A) ²=t^(T)S_(A)t, where S_(w) and S_(A) are anintra-class scatter matrix and a between-class scatter matrix of a givensample, and t is a mapping transformation vector; and

S203: the mapping transformation vector t is set as a special value ofthe ratio σ_(A) ²/σ_(W) ² of the between-class variance and theintra-class variance, that is, λ=σ_(A) ²/σ_(W)²=t^(T)S_(A)t/t^(T)S_(w)t, when the mapping transformation vector tapproximates a maximum value, (S_(A)−ΛS_(W)) T=0, where A represents adiagonal matrix consisting of all feature values λ, and a matrix Tcomposed of all column vectors t is a desired ideal transformationmatrix.

As a preferred technical solution of the present invention, in stepS300, a correlation relationship of the warehouse in the port isextracted, the correlation relationship includes a point feature, a linefeature and a plane feature included in spatial features; a hierarchicalrelationship feature of the correlation relationship is acquired byextracting a hierarchy attribute of the remote sensing image based on aspectral feature of the remote sensing image.

As a preferred technical solution of the present invention, thecorrelation relationship of the warehouse in the port includes a roadrelationship, a transshipment square relationship and an enclosurerelationship of the warehouse in the port, and attributes of a whole areextracted using a spatial correlation relationship of the warehouse inthe port.

As a preferred technical solution of the present invention, in stepS400, the visually sensitive image includes a gray level, colors, edges,textures and a motion, a visual saliency map of each position in a sceneimage is obtained according to synthesized features, and the mutualcompetition of a plurality of the visual saliency maps transfers theinhibition of return of focus.

As a preferred technical solution of the present invention, thecompetition and inhibition of the visual saliency map includes thefollowing steps:

S401: selecting a plurality of parallel and separable feature maps fromthe feature point set, and recording a hierarchy attribute of eachposition in a feature dimension on a feature map to obtain the saliencyof each position in different feature dimensions;

S402: merging saliencies of different feature maps to obtain a totalsaliency measure, and guiding a visual attention process; and

S403: dynamically selecting, through a WTA network, the position withthe highest saliency from the saliency map as the Focus Of Attention(FOA), and then performing the processing circularly through theinhibition of return until a real scene image is obtained.

As a preferred technical solution of the present invention, the methodfurther includes a step S500 of tracking a nonlinear filtering feature,including: separately extracting the filtering feature obtained in stepS100 using hierarchical image attributes extracted through the executionof the foregoing four steps, and performing a tracking in a remotesensing analysis image according to a texture feature extractedaccording to a hierarchical analysis to compensate for an attribute thatcannot be directly extracted by tracking a texture feature, so as toform an interpreted remote sensing image, and comparing the formedremote sensing image with a real scene image in step S400 so as toremove an inaccurate tracked texture and keep a rational trackedtexture.

Compared with the prior art, the present invention has the followingbeneficial effects: by extracting the original attribute using a texturefeature and dividing edge lines of a coast of a port using a filter, thepresent invention timely removes data that does not need to be processedand thus reduces the amount of the data sequentially processed; afterperforming the foregoing processing, the present invention performs a CAtransformation to cause the remote sensing image data to be capable ofbeing hierarchized, and then hierarchizes the remote sensing image datausing insufficient spectrum information according to a spatial positionfeature of a warehouse to obtain principal component images of differenthierarchies, obtains attributes of a single entity using a spatialrelationship among the principal component images and thereby obtainsattributes of a whole, thus, the present invention has a highpertinence; moreover, the present invention accurately acquiresattributes of a warehouse in a port using a WTA visual rapid adaptationselection algorithm after forming a feature point set, and uses a trackoptimization algorithm to compensate for distorted data or data that isnot acquired through remote sensing to form a complete interpretedimage, thus avoiding the use of a correction algorithm and thecalculation of an approximate interpolation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating a flow according to thepresent invention;

FIG. 2 is a diagram illustrating a flow of extracting a feature by aGabor filter according to the present invention;

FIG. 3 is a schematic diagram illustrating a structure of a visual modelaccording to the present invention; and

FIG. 4 is a schematic diagram illustrating a competition and inhibitionstructure of a visual saliency map according to the present invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Technical solutions of the present invention will be described clearlyand completely below in conjunction with accompanying drawings set forththerein, and apparently, the embodiments described herein are merely apart of, but not all of the embodiments of the present invention. Allother embodiments devised by those of ordinary skill without anycreative work based on the embodiments described herein should fallwithin the scope of the present invention.

Embodiment

As shown in FIG. 1-FIG. 3, the present invention provides a method ofextracting a warehouse in a port from a hierarchically screened remotesensing image, comprising the following steps:

S100: extracting a coastline of the port based on an active contourmodel, successively performing texture feature recognition on any regionin a remote sensing image to form a sea area texture region and anirregular texture region, and extracting edge lines of a coastline ofthe port using a filter algorithm.

In step S100, a gray level co-occurrence matrix is used to recognize atexture feature, the specific recognition includes the following steps:

S101: optionally selecting a region of the remote sensing image, andsetting that the region has L gray level values, in this case, a graylevel co-occurrence matrix corresponding to the region is a matrixhaving LXL orders;

S102: selecting an optional position (i,j) in the matrix, wherein (I,j=1, 2 . . . . , L), an element at the optional position is a pixel at afixed distance from a pixel having a gray level of i and has a graylevel of j, wherein the following fixed positional relationship existsbetween the two pixels: ζ=(DX, DY), where ζ is a displacement, and DXand XY are distances in two directions; and

S103: extracting, according to a positional relationship between thegray level co-occurrence matrixes, a texture feature quantity such as anAngular Second Moment (ASM) and a contrast CON, wherein ASM=Σ_(i=1)^(L)Σ_(j=1) ^(L)P (i, j) and CON=Σ_(n=1) ^(L)n²[Σ_(i=1) ^(L)Σ_(j=1)^(L)P(i, j)]|i−j|, where P is a feature vector at the position (i,j),and n is the number of times extraction is performed.

The gray level co-occurrence matrix mentioned in the foregoing steps isa common means for processing a texture feature of a remote sensingimage, and a gray level co-occurrence matrix is used herein forprocessing a texture feature mainly for the following reasons:

1: texture features of local image regions should be counted before thegray level co-occurrence matrix performs a texture feature analysis, andthe extraction method provided herein needs to perform an extractionoperation for a plurality of times using local features, thus, theextraction method provided herein is capable of performing an extractionoperation in the original remote sensing image and directly using theextracted information in subsequent steps;

2: in the use of the gray level co-occurrence matrix, generally, morethan one texture feature is extracted in the gray level co-occurrencematrix, thus, a plurality of texture features can be used herein as abasis for a multi-hierarchical screening, moreover, it is common thatsome hierarchies lose image information during a multi-hierarchicalscreening, however, the extraction of a plurality of texture featurescan compensate for the lost information to a certain extent, thusfurther improving the quality of image extraction;

3: the most important point is that most of the texture featuresextracted using a gray level co-occurrence matrix are related with eachother, that is, the texture features extracted using a gray levelco-occurrence matrix can visually reflect a spatial relationship whoseapplication is emphasized herein and which is even a basis for theaccurate recognition of a warehouse in a port; in addition tofacilitating the direct implementation of a subsequent operation,decreasing unnecessary calculation, and increasing the speed ofcalculation, the pre-analysis of a corresponding texture feature is alsoadvantaged in being independent from a spatial relationship in an actualoperation although capable of visually embodying a spatial relationshipof textures, therefore, as an earlier data processing, the pre-analysis,although average in texture feature recognition, reduces the amount ofthe data processed, increases the speed of operation, and can lay afoundation for a subsequent accurate recognition.

For the filter algorithm used in step S100, it should also be noted thatin this step, due to the selection of a common Gabor filter as a filteralgorithm, optimizing edge lines of a coast in a port using a filteralgorithm refers specifically to: first, obtaining the discrete dataobtained in the foregoing step, selecting, using a discretized Gabortemplate matrix and an image data matrix convolution, the lowest centerfrequency when extracting an image feature using a filter, andperforming a frequency spectrum superposition calculation again tocalculate a filtered image.

In the actual application of the Gabor filter algorithm, it should beemphasized herein that it is well known that a big convolution matrixwill increase a computation burden sharply, this problem exists in thepresent invention as well, for this sake, a convolution matrix needs tobe optimized further to conquer the problem of heave computation burden,and as shown in FIG. 2, the optimization is specifically realized by:

first, setting Fourier transformations of two convolution matrixes f₁and f₂ as F₁ and F₂, in this case, the following equations are obtained:F₁=fft (f₁), and F₂=fft (f₂);

according to the convolution theorem, the following equation isobtained: conv (f₁, f₂)=ifft (F₁·*F₂), where cony represents aconvolution, fft represents a Fourier transformation, ifft represents aninverse transformation of a Fourier transformation, and F₁·*F₂represents the multiplying of corresponding elements in two matrixes F₁and F₂.

By performing an optimization operation through the execution of theforegoing steps, the amount of calculation conducted to extractmulti-hierarchical data is remarkably reduced, thus significantlyincreasing the efficiency of calculation, improving the actual handlingcapacity, and preventing calculation from being circularly repeatedredundantly.

S200: extracting principal component images of a plurality ofhierarchies using insufficient spectrum features, optionally selecting asample of the irregular texture region, and forming, through a CAtransformation, principal component images of different hierarchies withdifferent difference values by taking the ratio of a between-classdifference to an intra-class difference being maximum as an optimizationcondition;

The CA transformation specifically refers to a method for thediscriminant analysis of feature extraction, which is applied toextracting a feature and capable of maximizing the ratio of abetween-class variance of any data set to an intra-class variance of thedata set to ensure the maximum separability of the data set.

As a canonical analysis transformation (that is, a method for thediscriminant analysis of feature extraction) is an orthogonal lineartransformation based on a classified statistic feature obtained througha sample analysis, in step S200, before the CA transformation isexecuted, the maximized ratio of a between-class variance of an optionaldata set to an intra-class variance of the data set is extractedaccording to the following linear transformation formula: Y=TX, where Tis an ideal transformation matrix, so as to ensure the maximumseparability of the data set to provide optimized basic data for the CAtransformation.

A specific algorithm of the ideal transformation matrix is as follows:

S201: σ_(A) is set as a standard deviation of a class 1 and a class 2obtained after the transformation, σ_(w1) and σ_(w2) are set asintra-class standard deviations of the class 1 and the class 2, andσ_(w) is set as the average value of σ_(w1) and σ_(w2);

S202: the relationship between a transformed variance and anuntransformed variance is as follows:

σ_(w) ²=t^(T)S_(w)t, σ_(A) ²=t^(T)S_(A)t, where S_(w) and S_(A) are anintra-class scatter matrix and a between-class scatter matrix of a givensample, and t is a mapping transformation vector; and

S203: the mapping transformation vector t is set as a special value ofthe ratio σ_(A) ²/σ_(W) ² of the between-class variance and theintra-class variance, that is, λ=σ_(A) ²/σ_(W)²=t^(T)S_(A)t/t^(T)S_(w)t, when the mapping transformation vector tapproximates a maximum value, (S_(A)−ΛS_(W)) T=0, where A represents adiagonal matrix consisting of all feature values λ, and a matrix Tcomposed of all column vectors t is a desired ideal transformationmatrix.

To sum up, by taking the ratio of a between-class difference to anintra-class difference being maximum as an optimization condition, theCA transformation allows a first model corresponding to a maximumfeature value to contain maximum separable information, and so on and soforth, a plurality of separable information axes can be obtained throughthe CA transformation in a plurality of dimensions, in this way,principle component images of a plurality of hierarchies can beextracted using insufficient spectrum features, moreover, it also shouldbe noted that the CA transformation also decreases the number of thedimensions of a data space while increasing the separability of a classand thus reduces the complexity of an actual operation.

In the foregoing steps, the use of the CA transformation causes dataconcentrated in a remote sensing image to be separable, that is, thedata subjected to the CA transformation have an excellent separabilityso that data can be hierarchically extracted without loss in subsequenttransformations, resulting in that principle component images of theoriginal remote sensing image can be hierarchically extracted.

Step S300: accurately recognizing the warehouse in the port using aspatial relationship feature, extracting a correlation relationship ofthe warehouse in the port from the principle component images, thecorrelation relationship of the warehouse in the port includes a roadrelationship, a transshipment square relationship and an enclosurerelationship of the warehouse in the port, extracting attributes of awhole using the spatial correlation relationship of the warehouse in theport, and forming a feature point set with recognized warehouses to beanalyzed.

Spatial feature, which is seldom used in remote sensing imageprocessing, is mainly realized as a relationship pattern in a remotesensing image, that is, in a specific remote sensing image analysis, thefinal recognition of a target is realized using correlated features, ina remote sensing image, it is not easy to recognize attributes of anentity in a certain relationship by separately observing the entity,however, when a correlation of spatial relationship is introduced,attributes of an entity can be known using the correlated spatialrelationship, and even attributes of a whole consisting of entities canbe recognized using a structural feature and a relationship feature.

In step S300, a correlation relationship of the warehouse in the port isextracted, the correlation relationship includes a point feature, a linefeature and a plane feature included in spatial features; a hierarchicalrelationship feature of the correlation relationship is acquired byextracting a hierarchy attribute of the remote sensing image based on aspectral feature of the remote sensing image.

S400: extracting a feature of a visually sensitive image from a sceneimage based on WTA visual rapid adaptation selection to obtain afeedback selection of a real scene image to extract the warehouse in theport.

As shown in FIG. 4, in step S400, the visually sensitive image includesa gray level, colors, edges, textures and a motion, a visual saliencymap of each position in a scene image is obtained according tosynthesized features, and the mutual competition of a plurality of thevisual saliency maps transfers the inhibition of return of focus.

the competition and inhibition of the visual saliency map includes thefollowing steps:

S401: selecting a plurality of parallel and separable feature maps fromthe feature point set, and recording a hierarchy attribute of eachposition in a feature dimension on a feature map to obtain the saliencyof each position in different feature dimensions;

S402: merging saliencies of different feature maps to obtain a totalsaliency measure, and guiding a visual attention process; and

S403: dynamically selecting, through a WTA network, the position withthe highest saliency from the saliency map as the Focus Of Attention(FOA), and then performing the processing circularly through theinhibition of return until a real scene image is obtained.

Moreover, in the present invention, it also should be noted that themethod further includes a step S500 of tracking a nonlinear filteringfeature, including: separately extracting the filtering feature obtainedin step S100 using hierarchical image attributes extracted through theforegoing four steps, and performing a tracking in a remote sensinganalysis image according to a texture feature extracted according to ahierarchical analysis to compensate for an attribute that cannot bedirectly extracted by tracking a texture feature, so as to form aninterpreted remote sensing image, and comparing the formed remotesensing image with a real scene image in step S400 after forming theinterpreted remote sensing image so as to remove an inaccurate trackedtexture and keep a rational tracked texture.

In conclusion, the main features of the present invention lie in that:by extracting the original attribute using a texture feature anddividing edge lines of a coast of a port using a filter, the presentinvention timely removes data that does not need to be processed andthus reduces the amount of the data sequentially processed; afterperforming the foregoing processing, the present invention performs a CAtransformation to cause the remote sensing image data to be capable ofbeing hierarchized, and then hierarchizes the remote sensing image datausing insufficient spectrum information according to a spatial positionfeature of a warehouse to obtain principal component images of differenthierarchies, obtains attributes of a single entity using a spatialrelationship among the principal component images and thereby obtainsattributes of a whole, thus, the present invention has a highpertinence; moreover, the present invention accurately acquiresattributes of a warehouse in a port using a WTA visual rapid adaptationselection algorithm after forming a feature point set, and uses a trackoptimization algorithm to compensate for distorted data or data that isnot acquired through remote sensing to form a complete interpretedimage, thus avoiding the use of a correction algorithm and thecalculation of an approximate interpolation.

It is apparent for those skilled in the art that the present inventionis not limited to details of the foregoing exemplary embodiments and thepresent invention can be realized in other specific forms withoutdeparting from the spirit or basic features of the present invention.Thus, the embodiments should be regarded as exemplary but not limitativein any aspect; because the scope of the present invention is defined byappended claims but not the foregoing description, the present inventionis intended to cover all the variations falling within the meaning andscope of an equivalent of the claims. Any reference symbol in the claimsshould not be construed as limiting a relevant claim.

What is claimed is:
 1. A method of extracting a warehouse in a port froma hierarchically screened remote sensing image, comprising the followingsteps: S100: extracting a coastline of the port based on an activecontour model, successively performing a texture feature recognition onany region in a remote sensing image to form a sea area texture regionand an irregular texture region, and extracting edge lines of thecoastline of the port; S200: extracting principal component images of aplurality of hierarchies using insufficient spectrum features, selectinga sample of the irregular texture region, and forming, through a CAtransformation, principal component images of different hierarchies withdifferent difference values by taking a ratio of a between-classdifference to an intra-class difference being maximum as an optimizationcondition; S300: accurately recognizing the warehouse in the port usinga spatial relationship feature, extracting a correlation relationship ofthe warehouse in the port from the principle component images, andforming a feature point set with recognized warehouses to be analyzed;and S400: extracting a feature of a visually sensitive image from thefeature point set through a scene image based on WTA visual rapidadaptation selection to obtain a feedback selection of a real sceneimage to extract the warehouse in the port.
 2. The method of extractingthe warehouse in the port from the hierarchically screened remotesensing image according to claim 1, wherein in step S100, a gray levelco-occurrence matrix method is used for the texture feature recognition,and the texture feature recognition comprises the following steps: S101:selecting a region of the remote sensing image, and setting that theregion has L gray level values, and a gray level co-occurrence matrixcorresponding to the region is a matrix having L×L orders; S102:selecting a position (i, j) in the matrix, where (i, j=1, 2, . . . , L),wherein, an element at the optional position is a first pixel at a fixeddistance from a second pixel having a gray level of i and a gray levelof j, wherein the following fixed positional relationship exists betweenthe first pixel and the second pixel: ζ=(DX, DY), where ζ is adisplacement, and DX and DY are distances in two directions; S103:extracting, according to a positional relationship between gray levelco-occurrence matrixes, a texture feature quantity including an AngularSecond Moment (ASM) and a contrast CON, wherein ASM=Σ_(i=1) ^(L)Σ_(j=1)^(L)P² (i, j), and CON=Σ_(n=1) ^(L)n²[Σ_(i=1) ^(L)Σ_(j=1) ^(L)P(i,j)]|i−j|, where P is a feature vector at the position (i,j), and n isthe number of times the extraction is performed.
 3. The method ofextracting the warehouse in the ort from the hierarchically screenedremote sensing image according to claim 1, wherein in step S100,extracting edge lines of the coast of the port between the sea areatexture region and the irregular texture region using the filteralgorithm and optimizing the edge lines of the coast of the port usingthe filter algorithm comprises the following steps: first, acquiringdiscrete data of a texture feature and selecting a lowest centerfrequency when extracting an image feature in a filter using adiscretized Gabor template matrix and an image data matrix convolution,and then carrying out a frequency spectrum superposition calculationagain to obtain a filtered image.
 4. The method of extracting thewarehouse in the port from the hierarchically screened remote sensingimage according to claim 1, wherein in step S200, before the CAtransformation is executed, a maximized ratio of a between-classvariance of an optional data set to an intra-class variance of theoptional data set is extracted according to the following lineartransformation formula: Y=TX, where T is an ideal transformation matrix,so as to ensure the maximum separability of the optional data set toprovide optimized basic data for the CA transformation
 5. The method ofextracting the warehouse in the port from the hierarchically screenedremote sensing image according to claim 4, wherein a specific algorithmof the ideal transformation matrix is as follows: S201: σ_(A) is set asa standard deviation of a class 1 and a class 2 obtained after the CAtransformation, σ_(w1) and σ_(w2) are set as intra-class standarddeviations of the class 1 and the class 2, and σ_(w) is set as anaverage value of σ_(w1) and σ_(w2); S202: a relationship between atransformed variance and an untransformed variance is as follows: σ_(w)²=t^(T)S_(w)t, σ_(A) ²=t^(T)S_(A)t, where S_(w) and S_(A) are anintra-class scatter matrix and a between-class scatter matrix of a givensample, and t is a mapping transformation vector; and S203: the mappingtransformation vector t is set as a special value of a ratio σ_(A)²/aσ_(W) ², of the between-class variance and the intra-class variance,where, λ=σ_(A) ²/σ_(W) ²=t^(T)S_(A)t/t^(T)S_(w)t, when the mappingtransformation vector t approximates a maximum value, (S_(A)−ΛS_(W))T=0, where Λ represents a diagonal matrix consisting of all featurevalues λ, and a matrix T composed of all column vectors t is a desiredideal transformation matrix.
 6. The method of extracting the warehousein the port from the hierarchically screened remote sensing imageaccording to claim 1, wherein in step S300, a correlation relationshipof the warehouse in the port is extracted, the correlation relationshipincludes a point feature, a line feature and a plane feature included inspatial features; a hierarchical relationship feature of the correlationrelationship is acquired by extracting a hierarchy attribute of theremote sensing image based on a spectral feature of the remote sensingimage.
 7. The method of extracting the warehouse in the port from thehierarchically screened remote sensing image according to claim 6,wherein the correlation relationship of the warehouse in the portincludes a road relationship, a transshipment square relationship and anenclosure relationship of the warehouse in the port, and attributes of awhole are extracted using a spatial correlation relationship of thewarehouse in the port.
 8. The method of extracting the warehouse in theport from the hierarchically screened remote sensing image according toclaim 1, wherein in step S400, the visually sensitive image includes agray level, colors, edges, textures and a motion, a visual saliency mapof each position in a scene image is obtained according to synthesizedfeatures, and a mutual competition of a plurality of the visual saliencymaps transfers an inhibition of return of focus.
 9. The method ofextracting the warehouse in the port from the hierarchically screenedremote sensing image according to claim 8, wherein the mutualcompetition and the inhibition of the visual saliency map comprises thefollowing steps: S401: selecting a plurality of parallel and separablefeature maps from the feature point set, and recording a hierarchyattribute of each position in a feature dimension on a feature map toobtain a saliency of each position in different feature dimensions;S402: merging saliencies of different feature maps to obtain a totalsaliency measure, and guiding a visual attention process; and S403:dynamically selecting, through a WTA network, a position with thehighest saliency from the saliency map as a Focus Of Attention (FOA),and then performing the processing circularly through the inhibition ofreturn until a real scene image is obtained.
 10. The method ofextracting the warehouse in the port from the hierarchically screenedremote sensing image according to claim 1, further comprising: a stepS500 of tracking a nonlinear filtering feature, comprising: separatelyextracting a filtering feature obtained in step S100 using hierarchicalimage attributes extracted through S100, S200, S300 and S400, andperforming a tracking in a remote sensing analysis image according to atexture feature extracted according to a hierarchical analysis tocompensate for an attribute that cannot be directly extracted bytracking the texture feature to form an interpreted remote sensingimage, and comparing the interpreted remote sensing image after beingformed with a real scene image in step S400 to remove an inaccuratetracked texture and keep a rational tracked texture.